Computer Science and Mathematics

Abstract: Open Educational Resources (OER) are teaching, learning, and research materials that are freely accessible and openly licensed, allowing users to use, adapt, and redistribute them with few or no restrictions. This article presents a quality evaluation experience over a set of OERs, a prior step to a clusterization process based on specific criteria. The evaluators have been students, from a teacher training master's program, that were instructed in concepts related to Open Learning, digital educational repositories, design and production processes for digital educational materials, and OER quality standards. The experiment consisted of evaluating the OERs stored in an online repository called Procomun, resources associated with the discipline of Computer Science. The resources have been created by both professionals and the students themselves, with the aim of comparing production quality levels and various specific criteria between them. For this purpose, two types of evaluations were carried out. First, the quality of the repository’s semantic tagging, based on the Learning Object Metadata (LOM) standard, was assessed using the Metadata Quality Assessment Model. Second, the UNE 71362 standard was applied to a selected collection of OERs obtaining a set of spider diagrams. Finally, to evaluate the value of the quality assessment itself, two types of processes were carried out: students acted as evaluators of the resources they had produced themselves (as a self-assessment task), and peer assessment was also carried out by other students. The article describes the entire experience, the evaluation process, the quality framework and the results obtained in the experimentation.

Abstract: Although pairwise comparison-based MCDA methodologies such as AHP, ANP, and Fuzzy AHP have matured analytically, their adoption in group decision-making remains limited by a software gap: there is no open-source platform that integrates all three within a single architecture, and there is a lack of real-time multi-expert collaboration on the aggregation of pairwise comparison matrices. This paper introduces ThinkDecision, an open-source web platform that integrates client- side computation engines for all three methodologies (O(n²) for AHP/Fuzzy AHP, O(k · N³) forANP) with WebSocket synchronization supporting multi-expert AIJ/AIP aggregation at a latency of <50 ms. Validation shows a maximum deviation of 0.41–0.94% and machine precision (∼ 10⁻¹⁶) for aggregation, while latency remains below 100 ms. A case study on ERP vendor selection revealed rank reversal phenomena and a heterogeneity threshold (D_L1 ≈ 0.20) above which AIJ and AIP diverge, demonstrating that the choice of methodology and aggregation strategy can materially alter decision outcomes and inconsistencies detectable only through multi-methodological evaluations such as ThinkDecision.

Abstract: GLAM (Galleries, Libraries, Archives, and Museums) institutions have been making digital collections available for decades. New initiatives to publish, preserve, and reuse data have emerged, with collaborative projects such as Wikidata playing a significant role. This work provides a framework for extracting multimodal collections as data, using Wikidata as the primary source, along with a selection of research scenarios for reusing this data in line with emergent trends in data publication and reuse. Results showed that current trends in data preservation can be achieved through open-source code, cloud services, and collaborative platforms. Future work to be explored includes adopting best practices for provenance documentation and refining reuse scenarios.

Abstract: In an age of pervasive connectivity and rapidly evolving cyber threats, cybersecurity has become a transversal competency in engineering education rather than a niche specialism. This paper presents MoonPhase, a multifunctional portable device based on a Raspberry Pi platform, designed to support experiential cybersecurity learning in engineering education contexts by integrating offensive, defensive and educational modes into a single physical artefact. The work builds on a PRISMA-based systematic literature review and a state-of-the-art analysis of portable cybersecurity tools to derive design requirements that balance realism, safety and pedagogical alignment. MoonPhase combines sub-GHz replay capabilities, 2.4 GHz interference and monitoring, fake Wi-Fi access points, packet sniffing and network scanning in a compact, menu-driven platform that can be deployed in regular laboratories and outreach workshops. The paper describes the hardware and software architecture of the device and, more importantly, its instructional framing, outlining learning outcomes, example lab sessions and assessment strategies focused on cybersecurity literacy, systems thinking and ethical awareness. This study reports on the design and implementation of MoonPhase and presents a detailed conceptual and evaluation framework; empirical evidence from classroom deployments will be addressed in subsequent work. The device is positioned as a replicable open educational resource that brings students closer to realistic attack and defence scenarios in controlled settings.

Abstract: This article presents the design and deployment of ClimaBogotá v1.2, a climate prediction system tailored for high-altitude urban micro-zones in Bogotá, Colombia. The system combines low-cost IoT sensing, machine learning modeling, and cloud-based orchestration to enable scalable and affordable meteorological forecasting. Its architecture comprises Raspberry Pi-based weather stations, a Random Forest model trained on engineered temporal features, and an n8n-driven automation pipeline for real-time inference and dissemination via Telegram, PostgreSQL, and Grafana. With a Mean Absolute Error of 2.59°C and an R² of 0.6286 on a 30-minute forecast horizon, the system demonstrates both predictive reliability and operational feasibility using free-tier cloud resources. Unlike traditional weather systems, ClimaBogotá emphasizes modularity, adaptability, and cost-efficiency, offering a replicable framework for decentralized climate monitoring in data-scarce urban environments. Temporal misalignment between sensor nodes was identified as the primary constraint, informing future enhancements toward distributed learning strategies.

Abstract: This study introduces a novel physical layer authentication technique for Internet of Things (IoT) networks, leveraging Channel State Information (CSI) data from Wi-Fi signals to distinguish between authorized and unauthorized nodes, thereby enhancing security without compromising performance. Its novelty lies in the integrated framework that employs Non-negative Matrix Factorization (NMF) for efficient feature selection and a Gaussian Mixture Model (GMM) to identify complex patterns within the CSI data, adapting to the dynamic nature of IoT networks. The model demonstrates exceptional classification proficiency, achieving an accuracy rate of 99.83% and a recall of 100%, which is important for critical applications such as cybersecurity and anomaly detection, where identifying threats is of key importance. Furthermore, the F1-score of 99.84% reflects a strong balance between precision and recall. From a practical standpoint, the system is designed for efficiency and minimal resource consumption, exhibiting good computational efficiency, reduced training duration, and lower energy consumption compared to more complex architectures such as CNN and CNN+LSTM. This balance of high performance and resource efficiency makes it particularly suitable for deployment in resource-constrained IoT environments.

Abstract: This paper examines how interest rate model specification affects annuity present value estimation. Using annual real interest rate data for the United States, Italy, and India from 1991 to 2021, we compare nine deterministic and stochastic interest rate models within a unified discrete-time valuation framework: the historical path, constant mean, piecewise constant, piecewise linear, cubic polynomial, piecewise cubic, ARIMA, Vasicek, and Cox–Ingersoll Ross (CIR) specifications. Structural breakpoints are identified using Bai–Perron tests, and annuity values are computed under the portfolio rate method. The principal contribution is a cross-country out-of-sample evaluation design that spans all nine specifications across three qualitatively distinct interest rate regimes, generating regime-contingent guidance for model selection. As part of this design, we apply and evaluate piecewise linear and piecewise cubic temporal interest-rate representations within the valuation framework, enabling structural regime shifts and within-regime dynamics to be captured simultaneously. Results show that model performance is strongly regime-dependent. In the United States, piecewise constant and piecewise linear models provide the best out-of-sample performance. In Italy, ARIMA outperforms all alternatives, with Diebold–Mariano tests confirming its statistically significant advantage. In India’s volatile, non-trending environment, trend-extrapolating deterministic models fail, while the constant mean and Vasicek models generalize better. A mortality-adjusted robustness check confirms that interest rate model risk remains material after survival probabilities are incorporated. Present value errors reach approximately 17%, demonstrating the practical importance of model selection for annuity pricing, insurance reserving, and pension liability management under Solvency II and IFRS 17.

Abstract: In this study, we introduce idempotent Neutrosophic Hypersoft Rough Fuzzy Matrices (INHSRFMs) and focus on a particular case, the INHSRFM of T-type. We derive various properties for both INHSRFM and INHSRFM of T-type and present a series of theorems that validate our results. To illustrate the application of these theorems, a numerical example is included. Additionally, we propose an algorithm designed to solve decision-making (DM) problems using NHSRFMs. The practical applicability of the proposed method is demonstrated through an example.

Abstract: In real-world decision-making, constructing mathematical models is often difficult because the data are incomplete, uncertain, or even contradictory. The neutrosophic refined set provides a robust and flexible approach for effectively handling and representing these types of uncertainties. Various studies have highlighted its significant applications in decision making. In this study, a power mean operator is introduced to aggregate multiple Neutrosophic Refined Sets (NRSs) into a Single-Valued Neutrosophic Set (SVNs). The core mathematical properties of the newly introduced neutrosophic refined power mean operator are established. Moreover, two categories of neutrosophic refined cross-entropy measures are presented: one adapted from the SVNs-cross-entropy measure, and the other specifically formulated for neutrosophic refined sets. By employing the defined measures, an innovative decision making strategy is developed under the neutrosophic refined set environment. To demonstrate the effectiveness and practical relevance of the grounded strategy a numerical example based on the selection of an educational stream is solved.

Abstract: Software as a Service (SaaS) has become a key enabler of digital transformation and e-government modernization through scalable, flexible, and cost-effective service delivery. However, evidence of SaaS adoption in Southern African Development Community (SADC) public sectors remains limited and uneven. This study systematically reviews literature published between 2015 and 2025 on SaaS adoption, digital readiness, infrastructure, policy environments, and institutional capacity across SADC member states. Using PRISMA-guided screening, 31 studies were synthesized through narrative thematic analysis informed by the Technology–Organisation–Environment (TOE) framework and Institutional Theory. The findings reveal significant disparities in SaaS readiness across the region. South Africa is the only country with substantial empirical evidence of public-sector SaaS adoption, while most member states demonstrate only indirect indicators of readiness, including ICT maturity and e-government development. Four major barriers were identified: infrastructure deficits, policy and regulatory fragmentation, institutional capacity constraints, and uneven regional readiness. The study also identifies a “readiness paradox,” whereby stricter data sovereignty regulations co-exist with inadequate infrastructure for compliant SaaS deployment. The study contributes a contextualized framework for sustainable SaaS adoption in SADC public sectors.

Abstract: With the increasing penetration of wind power, the uncertainty of wind power generation poses greater challenges to the secure operation of power grids. This paper proposes WindPower-SAFusion, an improved Informer-based model for wind power forecasting. The proposed model optimizes long-sequence modeling from three aspects. First, ProbSparse self-attention is adopted to reduce the computational complexity from O(L2) to O(LlogL). Second, a convolutional distillation encoder is introduced to compress the input sequence and highlight key temporal features. Third, a multivariate fusion and recursive multi-step forecasting framework is constructed. Using historical power and wind speed information, experiments are conducted on measured data from the Daliang Wind Farm in Guazhou, Gansu Province, China. The results show that the proposed model significantly outperforms several mainstream forecasting models in 1-day, 3-day, and 7-day forecasting tasks. Ablation experiments further demonstrate that each core module plays a critical role in improving forecasting accuracy and generalization performance. Therefore, the proposed method provides a technically feasible solution with promising engineering application potential for power grid dispatching and wind power management.

Abstract: The rapid growth of Internet of Things (IoT) ecosystems has significantly increased cybersecurity threats due to device heterogeneity, resource limitations, and exposure to distributed attacks. Although Federated Learning (FL) has emerged as a promising privacy-preserving machine learning paradigm for decentralized intrusion detection, existing FL approaches often suffer from non-independent and identically distributed (non-IID) data, communication inefficiency, adversarial attacks, and unstable convergence in heterogeneous IoT environments. This study proposes a Privacy-Enhanced Federated Learning (PEFL) framework for adaptive and secure intrusion detection in large-scale IoT networks. The framework integrated differential privacy, secure aggregation, adaptive client selection, trust-aware federated optimization, and edge-assisted hierarchical coordination to improve robustness, scalability, and communication efficiency. The framework was evaluated using benchmark cybersecurity datasets, including CICIDS2017, UNSW-NB15, TON_IoT, and Bot-IoT under heterogeneous and adversarial conditions. Experimental results established that the proposed PEFL framework achieved improved intrusion detection accuracy, faster convergence stability, enhanced resilience against poisoning attacks, and reduced communication overhead compared with conventional FL approaches such as FedAvg and FedProx. The findings further indicated that adaptive client selection and trust-aware aggregation significantly improve model reliability and robustness in resource-constrained IoT environments. This framework will contribute toward the development of scalable, privacy-preserving, and deployable federated intrusion detection systems for next-generation intelligent IoT infrastructures.

Abstract: The paper shows how the ideas of Archimedes about integrating “mechanical methods” and formal reasoning can be connected with the modern-day use of three computer programs – Wolfram Alpha, Maple, and Excel – in exploring topics from elementary theory of numbers. Explorations deal with subsequences of integer sequences through step-by-step elimination of every other term obtained on the previous step. This process, resembling the sieve of Eratosthenes, is applied to tetrahedral numbers appearing in the social context of the family therapy triangulation method. It is demonstrated that symbolic computations of Wolfram Alpha enable generalization in the construction of the sieves that is confirmed by Maple and a spreadsheet. The paper addresses one of the aims of the special issue by demonstrating the duality of mathematics and technology in the sense that whereas the latter facilitates new approaches to knowledge acquisition, the former can be used to improve the efficiency of computations by reflecting on the results made possible by those approaches. The activities advocate for the value of integrating ancient ideas, digital tools, and elementary number theory in the education of mathematics teachers. Reflective comments by teacher candidates are included as appropriate.

Abstract: This paper presents a comprehensive cross-era analysis of the algorithmic evolution of Large Language Models (LLMs) through four developmental epochs: Before Transformer (pre-2017), Transformer (post-2017), Instruction-tuned \& Open-source LLMs, and Multimodal Agents (2024-2025). A novel innovation pathway framework is introduced that traces causal relationships between architectural breakthroughs and emergent capabilities, addressing critical research gaps in three dimensions: (1) Cross-paradigm synthesis connecting statistical foundations to modern multimodal systems, (2) Causal innovation mapping demonstrating how architectural choices propagate through model generations, and (3) Cross-domain capability analysis quantifying transfer between representation learning, knowledge acquisition, behavioral alignment, and multimodal integration. This analysis reveals that LLM progression represents fundamental paradigm shifts rather than incremental improvements, with transformer architectures, human feedback mechanisms, and open-source ecosystems collectively enabling the transition from specialized NLP tools to general reasoning systems. We provide empirical evidence through case studies of capability emergence, quantify innovation impacts using performance metrics, and examine safety implications through recent jailbreak analysis and refusal mechanism studies. The contributions include: (a) a unified lifecycle synthesis with original analytical framework, (b) innovation trajectory mapping with causal pathway analysis, and (c) validated evolutionary principles for forecasting next-generation AI capabilities.

Abstract: Road traffic accidents pose a growing public safety challenge in rapidly urbanizing regions of Nigeria, where infrastructure development and traffic management often lag behind increasing vehicle use. This study investigates the spatial distribution and hotspot patterns of road traffic accidents in Jega Local Government Area, Kebbi State, Nigeria, using Inverse Distance Weighting (IDW) spatial interpolation. Georeferenced accident count data were analyzed through descriptive statistics, spatial visualization, and interpolation on a 200 × 200 grid with an edge buffer to minimize boundary effects. Accident hotspots were delineated using an 80th percentile threshold of interpolated intensity values. The results reveal a strongly clustered spatial structure, characterized by pronounced inequality in accident occurrence, where a small number of locations account for a disproportionate share of recorded accidents. IDW surfaces, contour maps, three-dimensional visualizations, and Google Earth-compatible outputs consistently identify high-risk zones around major junctions and traffic convergence areas. The findings demonstrate that IDW provides a transparent, computationally efficient, and operationally effective approach for accident hotspot identification in data-constrained urban settings. The study offers practical decision-support tools for targeted road safety interventions and contributes to evidence-based traffic management planning in developing urban environments.

Abstract: This paper proposes a decentralized data trading approach based on the Automated Market Maker (AMM) mechanism, aiming to break through the bottlenecks in data trading efficiency and fairness via the collaborative innovation of market-oriented pricing mechanisms and automated trading processes. We focus on constructing an automatic pricing and matching mechanism based on liquidity pools. Subsequently, mathematical modeling and simulations reveal slippage generation mechanisms in data liquidity pools under trading shocks and imbalances. To address these issues, a novel dual slippage optimization mechanism integrating dynamic trade splitting and alternating order sorting is proposed, which decomposes orders into sub-orders and reorganizes their timing, establishing a dynamic equilibrium model. Experiments show the method reduces average slippage amplitude from 2.1% to 0.5% and representing a 76.2% reduction, significantly enhancing price stability and market efficiency. This research explores the mechanism of applying AMM to data asset trading and overcomes AMM's limitations, providing a theoretical and empirical foundation for building low-cost, high-fairness data trading systems through mechanism innovation and technical optimization.

Abstract: Cybersecurity risk is commonly expressed through impact and likelihood, yet likelihood remains difficult to estimate because cyber incidents are underreported, heterogeneous datasets are weakly comparable, and attacker behaviour changes faster than conventional probability baselines. This article proposes a method for operationalising likelihood through a cyber-exposure profile that integrates external cyber knowledge and organisation-specific telemetry into a graph-based representation. The contribution is a formally specified artefact chain — from unified data model through organization-specific profiling, metric registry, likelihood scoring, and control prioritization — that operationalises four constructs grounded in incident evidence: exposure, traceability, motivation, and Systems Update. The pipeline provides a pathway from heterogeneous source evidence to a bounded likelihood indicator comparable across organizations and observation periods. An evaluation in 15 real organizations shows that those implementing the cyber-exposure profile were associated with reduced incident frequency and faster detection-and-response times, providing preliminary empirical support for the framework’s directional claims.

Abstract: While Large Language Models (LLMs) have shown great promise in transforming credit risk assess-ment, existing evaluation frameworks are almost exclusively concerned with general financial NLP tasks and neglect the specific reasoning needed by practitioners. To address this, we develop the Credit Context Log Understanding and Prediction Evaluation (CCLUPE) benchmark. Unlike the previous benchmarks, CCLUPE aims to capture and evaluate the intricate reasoning unique to each constituent of the Chinese credit market, where evaluations are heavily based on the integration and synthesis of complex transacted logs and the prediction of hidden financial behaviors. Unlike previous benchmarks, CCLUPE aims to capture and evaluate the intricate reasoning unique to each constituent of the Chinese credit market. Unlike previous benchmarks, CCLUPE aims to capture and evaluate the intricate reasoning unique to each constituent. CCLUPE boasts more than 4,000 premium samples segmented by individual and micro-enterprise customers and distributed among 7 principal log types and 16 sub log types. A comprehensive assessment process involving upwards of 20 professional annotators is enacted to guarantee the quality of the dataset. Moreover, we introduce Log-Score, a novel evaluation metric designed to incorporate log misunderstanding penalties and assess multifaceted competencies. Even the state-of-the-art models underperform when it comes to these high-stakes tasks. CCLUPE serves as a rigorous testbed for the next generation of financial LLMs, ensuring their robustness for deployment in complex real credit scenarios.

Abstract: We develop a metamathematical analogue of special and curved relativity built from exact witness architectures. For a proposition equipped with exact positive and negative witness channels, the corresponding positive and negative terminal directions are promoted to formal terminal meta-fibers. These play the role of null directions and generate a terminal cone together with an invariant interval dσ² = dU dV = dT² − dX². This yields a flat theory, Terminal-Fiber Relativity, in which Lorentz-type transformations arise as exactly the observer changes preserving the terminal interval and the oriented terminal cone. We then reinterpret the principal barrier theorems of exact witness architecture theory as relativistic laws: the Selection Jump Theorem becomes a universal null-propagation principle; reflection collapse forbids global internal inertial charts on Π₁-universal sectors; and Tarski and diagonal barriers forbid global arithmetic charts on truth-universal sectors. The second half of the paper extends the flat theory to curved meta-relativity. We define terminal-fiber manifolds, local null charts, occupancy fields, barrier fields, and a scalar curvature law in dimension 1+1. Because ordinary Einstein dynamics is trivial in two dimensions, the curved theory is governed instead by a conformal scalar equation sourced by barrier density and mixed terminal occupancy. We also formulate a higher-rank extension and a functorial packaging from exact witness architectures to terminal-fiber geometries. The result is not an empirical substitute for spacetime physics, but a geometric invariant theory of exact recognition.

Abstract: Generative molecular models such as diffusion models and graph neural networks are widely used in drug design. However, their black-box nature means they lack an explicit understanding of chemical rules (e.g., valency, charge, aromaticity), often generating chemically impossible structures such as pentavalent carbon. To address this issue, this paper proposes Atomic-SCS, an atom-level chemical rule scoring tool based on a symbolic approach. Atomic-SCS does not rely on data-driven training but directly applies IUPAC rules to independently score each atom across four dimensions: valency, charge, aromaticity, and ring strain. It outputs continuous scores (0 = fully compliant, 1 = severe violation), provides atom-level diagnostic reports, and generates prioritized repair suggestions sorted by severity. The tool supports three strictness levels (conservative, balanced, liberal) and three operation modes (assess, diagnose, repair), and can be accessed via a command-line interface or Python API. Validation on 100 normal and 100 problematic molecules shows that Atomic-SCS effectively distinguishes valid from invalid structures (Mann-Whitney U test, p < 1e-18). The scoring functions are continuous and can serve as reward signals in generative model training. On a standard CPU, scoring 100 molecules averaged 0.000071 s per molecule. This work provides a rule-based scoring tool for generative molecular design.